Now that organizations want to consider enterprise MDM implementations that account for unstructured and structured data and big data, new processes and policies need to be considered to handle increased data complexity, in parallel, your organization implements an enterprise data identification and tracking system that extends governance workflow across all systems, which helps the data stewards maintain compliance with jurisdictional data privacy and security regulations, and also, you can get visibility into all data – no matter where it resides – along with the critical business context you need to make informed decisions about data governance.
Integrated enterprise modeling, data cataloging and data literacy capabilities for fast and accurate insights to compliance, innovation and transformation, improved data quality – maintaining the quality of enterprise data is a key aspect of successful data governance, only then it ensures that data can be trusted and that people can be made accountable for any adverse event that happens because of low data quality.
Instead of seeing GDPR and data legislation as a bind, organizations with an enterprise-ready platform can use data governance and security as a competitive advantage, systems used for identity and access management include single sign-on systems, multifactor authentication and access management, moreover, a MDM provides a dynamic data governance platform that enables your organization to manage the master data requirement in efficiently following the data governance processes and policies.
Covering enterprise architecture customer journey mapping, IT strategy and planning, risk and compliance management, analytics and decisions, artificial intelligence, in-memory data, machine learning, predictive analytics, streaming analytics and visual analytics, effective data governance serves an important function within the enterprise, setting the parameters for data management and usage, creating processes for resolving data issues and enabling business users to make decisions based on high-quality data and well-managed information assets, ordinarily, data governance is defined as management of data to validate for accuracy as per the requirements, standards, or rules that an individual organization needs for individual business.
Ultimately, organizations want data to work for them and governance is an essential part of making data work for your business, implementing a sound data governance framework requires a thorough review of company policy, strategy, security and technology, and it is becoming more critical to hone the aspects of a comprehensive data governance model as new, also, data governance is then present in enterprise systems and databases, data warehouses, decision support databases, departmental systems, shadow systems, end-user databases, spreadsheets, and more.
By contrast, a data hub must have more than one source populating it, or more than one destination to which data is moved, or both multiple sources and destinations, additionally, having data quality as a focus is your organizations philosophy that aligns strategy, business culture, organization information, and technology in order to manage data to the benefit of your enterprise is a strategic need.
Governance means making sure enterprise data is authorized, organized, and permissioned in a database with as few errors as possible, while maintaining both privacy and security, the query and data integration tooling of a modern hub can reach beyond the hub to all data, old and new, traditional and modern, on premises and in the cloud — for insights and operations based on correlations of diverse data from distributed sources, as a matter of fact, for end-to-end data quality, data migration and data governance solutions, your build, fix and sustain approach helps your organization get their data clean and keep it clean, so that critical business decisions are based on high-quality, reliable data.
Treat both data and content as assets that deserve oversight, policies, management, and deployment, realizing that all data moves through life-cycle stages is central to designing data governance, as a rule, if your organization wants to firm up data quality, it must first establish effective data governance.